Exploring optical descriptors for rapid estimation of coastal sediment organic carbon and nearby land-use classifications via machine learning models.

Journal: Marine pollution bulletin
Published Date: Apr 1, 2024

Abstract

This study utilizes ultraviolet and fluorescence spectroscopic indices of dissolved organic matter (DOM) from sediments, combined with machine learning (ML) models, to develop an optimized predictive model for estimating sediment total organic carbon (TOC) and identifying adjacent land-use types in coastal sediments from the Yellow and Bohai Seas. Our results indicate that ML models surpass traditional regression techniques in estimating TOC and classifying land-use types. Penalized Least Squares Regression (PLR) and Cubist models show exceptional TOC estimation capabilities, with PLR exhibiting the lowest training error and Cubist achieving a correlation coefficient 0.79. In land-use classification, Support Vector Machines achieved 85.6 % accuracy in training and 92.2 % in testing. Maximum fluorescence intensity and ultraviolet absorbance at 254 nm were crucial factors influencing TOC variations in coastal sediments. This study underscores the efficacy of ML models utilizing DOM optical indices for near real-time estimation of marine sediment TOC and land-use classification.

Authors

  • Xuan Cuong Nguyen
    Laboratory of Energy and Environmental Science, Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam; Faculty of Environmental Chemical Engineering, Duy Tan University, Da Nang, 550000, Viet Nam.
  • Suhyeon Jang
    Department of Environment and Energy, Sejong University, Seoul 05006, South Korea.
  • Junsung Noh
    Department of Environment and Energy, Sejong University, Seoul 05006, South Korea.
  • Jong Seong Khim
    School of Earth and Environmental Sciences & Research Institute of Oceanography, Seoul National University, Seoul 08826, South Korea.
  • Junghyun Lee
    Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Bong-Oh Kwon
    Department of Marine Biotechnology, Kunsan National University, Kunsan 54150, Republic of Korea.
  • Tieyu Wang
    Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Shantou University, Shantou 515063, China.
  • Wenyou Hu
    Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
  • Xiaowei Zhang
  • Hai Bang Truong
    Optical Materials Research Group, Science and Technology Advanced Institute, Van Lang University, Ho Chi Minh City 700000, Viet Nam; Faculty of Applied Technology, School of Technology, Van Lang University, Ho Chi Minh City 70000, Viet Nam.
  • Jin Hur
    Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.

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